The International Symposium on Empirical Software Engineering and Measurement (ESEM) technical papers track features submissions that describe original, unpublished work in software engineering and software measurement, with a strong empirical foundation. Papers in this track should communicate fully developed research and related results. Strong emphasis should be given to the methodological aspects of the research and the assessment of the validity of the contributions.
- Make sure the paper follows the ACM two-column template (in Latex, use “sigconf”).
- Make sure your paper follows the double-blind instructions and does not reveal the authors’ identities.
Submissions should not be under consideration for publication or presentation elsewhere. In addition to the specific scope of this track, submissions may address any aspect of software engineering but must tackle the problem from an empirical perspective and using a rigorous empirical method, including:
- Empirical studies using qualitative, quantitative, and mixed methods
- Cross- and multi-disciplinary methods and studies
- Formal experiments and quasi-experiments
- Case studies, action research, ethnography and field studies
- Survey research
- Simulation studies
- Artifact studies
- Data mining using statistical and machine learning approaches
- Secondary and tertiary studies including
- Systematic literature reviews and rapid reviews, that include a strong synthesis part
- Meta-analyses, and qualitative, quantitative or structured syntheses of studies
- Replication of empirical studies and families of studies
Papers should be positioned in terms of research methodology and contribution in relation to established frameworks, e.g. https://link.springer.com/article/10.1007/s10664-020-09858-z, https://dl.acm.org/doi/10.1145/3241743 or https://github.com/acmsigsoft/EmpiricalStandards.
Topics commonly addressed using an empirical approach include, but are not limited to:
- Evaluation and comparison of software models, tools, techniques, and practices
- Modeling, measuring, and assessing product or process quality and productivity
- Continuous software engineering
- Software verification and validation, including analysis and testing
- Engineering of software systems which include machine learning components and data dependencies
- Applications of software engineering to different types of systems and domains (e.g. IoT, Industry 4.0, Context-awareness systems, Cyber-physical systems)
- Human factors, teamwork, and behavioral aspects of software engineering
We welcome submissions on these research meta-topics:
- Development, evaluation, and comparison of empirical approaches and methods
- Infrastructure for conducting empirical studies
- Techniques and tools for supporting empirical studies
- Empirically-based decision making
We also welcome submissions that:
- demonstrate multi-disciplinary work,
- transfer and apply empirical methods from other disciplines,
- replication studies, and
- studies with negative findings.
(All dates are end of the day, anywhere on earth)
Abstract 26 April 2024
Submission 6 May 2024
Notification 20 June 2024
Submission Link: TBA
Submissions to this track are limited to 10 pages excluding references and 12 pages with references and must be submitted through EasyChair by selecting the track “Technical Papers.”
All submissions must be written in English and must be submitted via EasyChair in the PDF format, and they must be formatted according to the ACM proceedings template, which can be found at ACM Proceedings Template (https://www.acm.org/publications/proceedings-template) or in Overleaf at https://www.overleaf.com/gallery/tagged/acm).
A structured abstract is required with the headings: Background, Aims, Method, Results, and Conclusions. Papers must contain an explicit description of the empirical strategy used or investigated. The submission must also comply with the ACM plagiarism policy and procedures (http://www.acm.org/publications/policies/plagiarism_policy). In particular, it must not have been published elsewhere and must not be under review elsewhere while under review for ESEM. The submission must also comply with the IEEE Policy on Authorship (http://ieeeauthorcenter.ieee.org/publish-with-ieee/publishing-ethics/).
ESEM 2024 Technical Track will employ a double-blind review process. Thus, submissions may not reveal their authors’ identities. The authors must make an acceptable effort to honor the double-blind review process. In particular, the authors’ names must be omitted from the submission and references to their prior work should be in the third person. More details on author ethics and peer review can be found at https://conferences.ieeeauthorcenter.ieee.org/.
All submissions will be peer-reviewed by at least three experts from the international program committee of each track and will receive an additional meta-review. Any papers that are outside the scope of the symposium, exceed the maximum number of pages for the respective category, or do not follow the formatting guidelines will be desk rejected without review. The PC members’ bidding information may be used to assess what is considered out of scope.
Finally, please note that each accepted contribution must have a minimum of one author registered by the deadline for the camera-ready submission for their respective paper type. Also, each paper must be presented by one of the authors. Failure to meet these criteria will result in the paper’s removal from the proceedings.
Openness in science is key to fostering progress via transparency, reproducibility, and replicability. While all submissions will undergo the same review process independent of whether or not they disclose their tools, data, and code, we expect authors to include a data availability statement in their submissions that either provides links to the open data/replication package or that explains that why data cannot be disclosed (e.g. due to the sensitivity of the data or due to existing non-disclosure agreements).
Authors who can make their data available are strongly encouraged to do so upon submission (either privately or publicly), but they can also disclose it upon acceptance (publicly).
- We expect authors to include a data availability statement in the submission (for instance, at the end of the introduction) explaining whether and where the data and related material is available and under which conditions the data/material can be accessed. If the authors cannot disclose industrial or otherwise non-public data, they should provide an explicit (short) explanation in the statement.
- For submissions based on open data sources, the publication of any cleaned or filtered data is mandatory.
- Where reasonable, we ask authors to provide elaborate explanations on how to navigate the data source and how to use it. It must be explained how the provided data, code, and tools are used in the steps of the method described in the paper. This includes:
- Study protocols, coding and transcription schemas, and further relevant information used in qualitative studies.
- Information on the code (incl. version information) or the data input/output relevant to every step of data cleaning and labeling, feature engineering, model training, and evaluation for quantitative analysis and/or machine learning studies.
Finally, we further ask the authors to follow the FAIR principles in open science when sharing their tools, data, and code, and recommend following the principles as outlined in the book chapter “Open Science in Software Engineering” https://link.springer.com/chapter/10.1007/978-3-030-32489-6_17.
Silverio Martínez-Fernández, Universitat Politècnica de Catalunya
Maya Daneva, University of Twente